WHERE NEW DATABASE TECHNOLOGIES FIT INTO THE ENTERPRISE

WHERE NEW DATABASE TECHNOLOGIES FIT INTO THE ENTERPRISE

1. Analytics and business intelligence:When it comes to analytics, the choice of database environment depends on the type of work being performed. Much of the unstructured data that comprises many analytics applications often reside within big data file systems, such as Hadoop.

2. Core business functions and enterprise transaction processing: Core business functions and enterprise transaction processing: This is where the relational database still rules. Basic enterprise functions—payments, billing, tracking receipts, deliverables and payables—are best handled in industrialclass databases built to interface with all core enterprise applications

3. IT operational data (logs, systems monitoring; IT professionals need databases they can quickly spin up while they are in the midst of writing, testing or deploying code. For these purposes, they do not want to function as accidental DBAs—they need lightweight, quickly displayable databases. In times gone by, Microsoft Excel spreadsheets or Access databases served this role.

4. IT operational data (logs, systems monitoring IT professionals need databases they can quickly spin up while they are in the midst of writing, testing or deploying code. For these purposes, they do not want to function as accidental DBAs—they need lightweight, quickly displayable databases. In times gone by, Microsoft Excel spreadsheets or Access databases served this role.

5. Special projects: Many projects taking place around enterprises are typically led by non-technical business managers and professionals, and thus need to be as simple and intuitive to use as possible. Microsoft Access databases typically filled this role, but lately, there have been a number of cloud-based databases that can also fulfill this role. In addition, the opensource Hadoop framework provides a cost-effective test bed for experimentation with datasets, enabling greater innovation with little additional cost. Among data managers responding

6. Peripheral/branch office support: Branch offices typically require smallfootprint databases that synch up with a central data environment, or, conversely, access via the network to a central database. As a branch location is likely to be transaction-focused, relational database management systems may be most effective here.

7. Web applications: Web applications generate a lot of session information, and for this purpose, NoSQL key-value databases may be the best bet, since they are simple and fast, operating close to internet time and quickly handling spikes in usage.

8. Ecommerce applications: As is the case mentioned above for web applications, NoSQL key-value store databases, in conjunction with back-end relational databases for payment transaction processing, are an effective combination.

9. Mobile applications: This is the great frontier of data, as more and more local client-side computing is now occurring through smartphones and tablets. As with embedded devices, mobile smartphones require databases that are capable of going offline, then syncing back up with a central database as needed.